Vaccine data, Cases data from the hospital, and the waste water
signal data has been loaded, cleaned, and then merged into one final
dataframe, final_data.
The variables have been log transformed.
The response variable observed_census_ICU_p_acute_care
has been renamed as y and the date has been renamed as ds to fit the
prophet model.
The dataset is divided into train and test set. The test set consist of last 3 days of data.
Adding other variable as regressors to the model.
Fitting the Prophet Model.
Forecasting 3 days into future
Checking last 6 days of forecast data
#> ds yhat yhat_lower yhat_upper
#> 407 2022-01-27 4.446182 3.620653 5.351394
#> 408 2022-01-28 4.467676 3.540469 5.340873
#> 409 2022-01-29 4.442024 3.557992 5.315185
#> 410 2022-01-30 4.616082 3.728929 5.539167
#> 411 2022-01-31 4.360744 3.435497 5.258591
#> 412 2022-02-01 4.471954 3.629431 5.348357
The plot of actual data and predicted data from Prophet forecast. The blue line is predicted data whereas the black dots are actual data.
Root Mean Squared Error on train data
#> [1] 18.32961
MAPE on train set:
#> [1] 0.4147914
Standard deviation of the actual data
#> [1] 35.92902
Plots comparing actual data and predicted data
RMSE on test data
#> [1] 11.68178
MAPE on test set
#> [1] 0.1360083
Plots comparing actual data and predicted data
The error metrics are low when model is regressed against only those who received 2nd dose in the older age groups.
The model trend shows a moderately strong linear increase in cases from January 2021 until January 2022. The extra regressor plot shows the additive effect of regressors and it shows that 2nd dose of vaccination in older age groups remains high until April/May 2021 and then shows a sharp decrease in July 2021 and then remains steady after July 2021 until January 2022. Weekly trend shows there are more hospitalizations on Tuesday and Thursday.